Jiles-Atherton-Based Hysteresis Identification of Joint Resistant Torque in Active Spacesuit Using SA-PSO Algorithm

Abstract

To eliminate the influence of spacesuits’ joint resistant torque on the operation of astronauts, an active spacesuit scheme based on the joint-assisted exoskeleton technology is proposed. Firstly, we develop a prototype of the upper limb exoskeleton robot and theoretically analyse the prototype to match astronauts’ motion behavior. Then, the Jiles-Atherton model is adopted to describe the hysteretic characteristic of joint resistant torque. Considering the parameter identification effects in the Jiles-Atherton model and the local optimum problem of the basic PSO (particle swarm optimization) algorithm, a SA- (simulated annealing-) PSO algorithm is proposed to identify the Jiles-Atherton model parameters. Compared with the modified PSO algorithm, the convergence rate of the designed SA-PSO algorithm is advanced by 6.25% and 20.29%, and the fitting accuracy is improved by 14.45% and 46.5% for upper limb joint model. Simulation results show that the identified J-A model can show good agreements with the measured experimental data and well predict the unknown joint resistance torque.

An explainable neural network integrating Jiles-Atherton and nonlinear auto-regressive exogenous models for modeling universal hysteresis

“…Further validations of the Jiles-Atherton model's versatility abound. Li and Dai [44] harnessed its capabilities to describe the hysteretic characteristics of joint moments.…”